1) Canonical correlation analysis (CCA) is a statistical method that analyzes the correlation relationship between two sets of multidimensional variables.
2) CCA finds linear transformations of the two sets of variables so that their correlation is maximized. This can be formulated as a generalized eigenvalue problem.
3) The number of dimensions of the transformed variables is determined using Bartlett's test, which tests the eigenvalues against a chi-squared distribution.
1) Canonical correlation analysis (CCA) is a statistical method that analyzes the correlation relationship between two sets of multidimensional variables.
2) CCA finds linear transformations of the two sets of variables so that their correlation is maximized. This can be formulated as a generalized eigenvalue problem.
3) The number of dimensions of the transformed variables is determined using Bartlett's test, which tests the eigenvalues against a chi-squared distribution.
北村大地, 小野順貴, "独立性基準を用いた非負値行列因子分解の効果的な初期値決定法," 日本音響学会 2016年春季研究発表会, 3-3-5, pp. 619-622, Kanagawa, March 2016.
Daichi Kitamura, Nobutaka Ono, "Statistical-independence-based effective initialization for nonnegative matrix factorization," Proceedings of 2016 Spring Meeting of Acoustical Society of Japan, 3-3-5, pp. 619-622, Kanagawa, March 2016 (in Japanese).
北村大地, 小野順貴, "独立性基準を用いた非負値行列因子分解の効果的な初期値決定法," 日本音響学会 2016年春季研究発表会, 3-3-5, pp. 619-622, Kanagawa, March 2016.
Daichi Kitamura, Nobutaka Ono, "Statistical-independence-based effective initialization for nonnegative matrix factorization," Proceedings of 2016 Spring Meeting of Acoustical Society of Japan, 3-3-5, pp. 619-622, Kanagawa, March 2016 (in Japanese).
1. USVSEG
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